The coincidence factor is a diversity metric used in electrical system design to estimate how much of the installed (connected) load is likely to occur simultaneously. In EV charging, this helps engineers and site planners realistically size feeders, panels, and grid connections by accounting for the fact that not all chargers will draw maximum power simultaneously.
What Is Coincidence Factor?
Coincidence factor is typically defined as:
Coincidence factor = maximum simultaneous demand ÷ sum of individual maximum demands
It is a value between 0 and 1:
– A value close to 1.0 means loads peak at the same time (high simultaneity)
– A lower value means loads are staggered (diverse usage), reducing peak demand versus total installed capacity
For example, a site with ten 22 kW chargers has a connected load of 220 kW, but its maximum simultaneous demand might be much lower, depending on user behavior and control systems.
Why Coincidence Factor Matters for EV Charging
EV charging projects are often constrained by available electrical capacity and grid upgrade cost. Coincidence factor matters because it supports:
– More accurate sizing of circuit breakers, feeders, and distribution boards
– Cost-effective grid connection planning without oversizing
– Better site feasibility decisions during early-stage design
– Smarter expansion planning (adding more charge points without upgrading supply)
– Improved economics and faster deployment in constrained locations
It is especially relevant for AC destination charging, where vehicles remain parked for long periods and charging start times vary.
How Coincidence Factor Is Used in Charger Site Design
Coincidence factor is used to convert installed charger power into a realistic peak demand estimate:
– Connected load = sum of charger nameplate ratings (kW or A)
– Estimated peak demand = connected load × coincidence factor
This peak demand is then compared to:
– Available building capacity
– Main fuse rating or service headroom
– Transformer and feeder limits
– Planned future expansion headroom
Designers often apply the coincidence factor alongside load-balancing and demand-management assumptions.
What Drives the Coincidence Factor in EV Charging
Coincidence factor is shaped by both behavior and technical control:
User Behavior and Site Type
– Workplace sites often have morning arrival clustering but long dwell times
– Retail sites may have shorter sessions and more random arrivals
– Residential sites can peak in evening hours
– Fleet depots may have highly synchronized charging windows
Different site archetypes can produce very different simultaneity patterns.
Charger Power and Vehicle Limits
Even if chargers are rated high, actual draw may be limited by:
– Vehicle onboard charger capacity (AC)
– Battery SoC and charging tapering behavior (especially for DC)
– Site power caps or dynamic allocation rules
Smart Charging and Load Management
Using dynamic load management reduces simultaneity by actively controlling power:
– Sharing available power across multiple connectors
– Prioritizing certain users or vehicles
– Scheduling charging to off-peak times
When smart charging is applied, the effective coincidence factor is often lower and more predictable.
Coincidence Factor vs Diversity Factor
These terms are related but different:
– Coincidence factor focuses on simultaneity (how much peaks overlap)
– Diversity factor is often expressed as the inverse relationship (sum of individual maxima ÷ maximum simultaneous demand)
Both are used to describe how real demand differs from installed capacity, but the ratio points in opposite directions.
Typical Applications in EV Charging Projects
Coincidence factor is commonly used for:
– Multi-bay workplace and destination AC charging design
– Residential building EV-ready upgrades
– Depot charging feasibility studies and expansion phases
– Public parking sites where future expansion is planned
It is most useful when combined with real usage data from charging session analytics to validate assumptions over time.
Common Pitfalls
– Assuming too low a coincidence factor without supporting data, causing undersized infrastructure
– Assuming too high a factor and oversizing capacity, increasing CAPEX unnecessarily
– Ignoring site-specific patterns like fleet shift changes or peak retail hours
– Treating the coincidence factor as fixed even after adding more chargers or changing pricing/access rules
– Not accounting for control failures (load management disabled or misconfigured)
Related Glossary Terms
Load Balancing
Dynamic Load Management
Charger Utilization Rate
Charging Session Analytics
Electrical Panels
Circuit Breakers
Charging Capacity Planning
Peak Charging Power